The use of digital signal processing for physiological signals has been an active long-term field of research. Various digital signal processing (DSP) techniques have been applied to physiological signal sources such as heartbeat, ECG/EKG, EMG, heart and lung sounds, and many others. In almost all cases however, the employed methods need considerable computation power leading to moderate to high levels of power consumption. Many portable devices have been built, but often they are not as miniaturized as they ideally could be.
As early as 1981, U.S. Pat. No. 4,263,919 reveals methods and systems of analog signal processing for heartbeat detection and artifact discrimination using ECG signals. U.S. Pat. No. 4,478,224 discloses a heartbeat rate measuring system for monitoring a patient's EKG signal with artifact rejection. It combines analog signal processing (ASP) with DSP on a microprocessor to estimate the heartbeat rate using a time-domain method. Similarly, U.S. Pat. No. 4,686,998 combines both ASP and DSP to measure the temperature and heartbeat remotely on a hand held battery-powered device.
U.S. Pat. No. 5,209,237 discloses detecting noisy physiological signals (like fetal heartbeat) using multiple sensors, and a combination of ASP and DSP noise cancellation techniques such as correlation cancellation and Wiener filtering.
As the use of DSP techniques in signal processing becomes more dominant, several inventions report implementations of more complicated DSP methods. These include U.S. Pat. Nos. 5,596,993, 5,666,959 and 6,245,025 B1 all pertaining to fetal heartbeat monitoring, and U.S. Pat. Nos. 5,908,393, and 6,262,943 B1 both discussing the reduction of noise in biological signals. More elaborate and recent multi-channel DSP techniques are disclosed in U.S. Pat. Nos. 6,551,251 B2, 6,662,043 B1, and 6,575,915 B2.
Adaptive noise cancellation (ANC) techniques have been extensively used to process physiological signals. U.S. Pat. Nos. 5,492,129 and 5,662,105 disclose the use of ANC methods for noise reduction in stethoscopes and physiological signals. In U.S. Pat. No. 6,650,917 B2, the use of various variants of ANC method for physiological signal processing (particularly for blood oxiometery measurements) is disclosed.
Active noise control is also suggested for signal processing in stethoscopes and similar devices in U.S. Pat. Nos. 5,610,987 and 5,737,433.
U.S. Pat. Nos. 5,243,992, 5,243,993, 5,365,934, 5,524,631, and 5,738,104 disclose heartbeat rate detection through the use of autocorrelation function estimation. It is notable that they all estimate the autocorrelation function in the time-domain.
Filterbanks have also been proposed for use in physiological signal processing (PSP). In a series of research papers from 1995 to 1999, Afonso et al. have disclosed the use of perfect reconstruction filterbanks to process the ECG signal (V. X Afonso et al., “Multirate processing of the ECG using filter banks”, Computers in Cardiology 1996, 8-11 Sep. 1996, pp. 245-248; V. X Afonso et al., “Filter bank-based of the stress ECG”, in Proc. 17th Annual Int. Conf. of the IEEE/EMBS, pp. 887-888 vol. 2, 20-23 Sep. 1995; V. X Afonso et al., “Comparing stress ECG enhancement algorithms”, in IEEE Eng. In Medicine and Biology, pp. 37-44, May/June 1996; V. X Afonso et al., “Filter bank-based ECG beat classification”, in Proc. 19th Annual Int. Conf. of the IEEE/EMBS, Oct. 30-Nov. 2, 1997; V. X Afonso et al., “ECG beat detection using filter banks”, IEEE Trans. on Biomedical Eng., Vol. 46, No. 2, pp. 192-202, February 1999). Other researchers have used similar methods as reported for example in S. O. Aase, “Filter bank design for subband ECG compression”, in Proc. 17th Annual Int. Conf. of the IEEE/EMBS, pp. 1382-1383, 1996; M. C. Aydin et al., “ECG data compression by sub-band coding”, IEEE Electronic Letters, Vol. 27, Issue: 14, pp. 359-360, 14 Feb. 1991.
However, current methods for processing physiological signals described above have inherent limitations when deployed in standalone instruments. For example, there is a long delay between the time when the signal occurs and when the processing completes. The conventional methods are not well suited for deployment on parallel systems. The conventional methods are not well suited for deployment on cost effective fixed-point (16 bit) systems. Although some conventional methods process in the frequency-domain, they do not allow independent subband processing. The conventional instruments are too big or heavy, and the power consumption is too high, limiting the portability of the systems. The output (including audio) quality is not sufficient. Feature extraction is not sufficiently robust. Due to low-power and small-size constraints, more efficient and complicated signal processing methods cannot be deployed.
It is therefore desirable to provide a new method and system, which can efficiently process signals including possible physiological signals, and can implement physiological signal processing on ultra low-power, small size and low-cost platform in real-time.